Erratum to “On the performance of some non-parametric estimators of the conditional survival function with interval-censored data” [Comput. Statstic Data Anal. 55 (12) (2011) 3355–3364]

2016 ◽  
Vol 104 ◽  
pp. 247
Author(s):  
Mohammad Hossein Dehghan ◽  
Thierry Duchesne
Keyword(s):  
Censored Data ◽  
Survival Function ◽  
Conditional Survival ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Conditional Survival Function ◽  
Non Parametric

Tutorial on methods for interval-censored data and their implementation in R

2009 ◽  
Vol 9 (4) ◽  
pp. 259-297 ◽  
Author(s):  
Guadalupe Gómez ◽  
M Luz Calle ◽  
Ramon Oller ◽  
Klaus Langohr
Keyword(s):  
Censored Data ◽  
Time Window ◽  
Interval Censoring ◽  
Right Censoring ◽  
Interval Censored Data ◽  
Survival Curves ◽  
Statistical Software ◽  
The Past ◽  
Interval Censored ◽  
Non Parametric

Interval censoring is encountered in many practical situations when the event of interest cannot be observed and it is only known to have occurred within a time window. The theory for the analysis of interval-censored data has been developed over the past three decades and several reviews have been written. However, it is still a common practice in medical and reliability studies to simplify the interval censoring structure of the data into a more standard right censoring situation by, for instance, imputing the midpoint of the censoring interval. The availability of software for right censoring might well be the main reason for this simplifying practice. In contrast, several methods have been developed to deal with interval-censored data and the corresponding algorithms to make the procedures feasible are scattered across the statistical software or remain behind the personal computers of many researchers. The purpose of this tutorial is to present, in a pedagogical and unified manner, the methodology and the available software for analyzing interval-censored data. The paper covers frequentist non-parametric, parametric and semiparametric estimating approaches, non-parametric tests for comparing survival curves and a section on simulation of interval-censored data. The methods and the software are described using the data from a dental study.

scholarly journals Regression Models for Interval Censored Data Using Parametric Pseudo-Observations

2020 ◽  
Author(s):  
Martin Nygård Johansen ◽  
Søren Lundbye-Christensen ◽  
Jacob Moesgaard Larsen ◽  
Erik Thorlund Parner
Keyword(s):  
Censored Data ◽  
Interval Censoring ◽  
Regression Modeling ◽  
Implantable Cardioverter Defibrillators ◽  
Interval Censored Data ◽  
Right Censored Data ◽  
Association Measures ◽  
Real Dataset ◽  
Interval Censored ◽  
Non Parametric

Abstract Background: Time-to-event data that is subject to interval censoring is common in the practice of medical research and versatile statistical methods for estimating associations in such settings have been limited. For right censored data, non-parametric pseudo-observations have been proposed as a basis for regression modeling with the possibility to use different association measures. In this article, we propose a method for calculating pseudo-observations for interval censored data. Methods: We develop an extension of a recently developed set of parametric pseudo-observations based on a spline-based flexible parametric estimator. The inherent competing risk issue with an interval censored event of interest necessitates the use of an illness-death model, and we formulate our method within this framework. To evaluate the empirical properties of the proposed method, we perform a simulation study and calculate pseudo-observations based on our method as well as alternative approaches. We also present an analysis of a real dataset on patients with implantable cardioverter-defibrillators who are monitored for the occurrence of a particular type of device failures by routine follow-up examinations. In this dataset, we have information on exact event times as well as the interval censored data, so we can compare analyses of pseudo-observations based on the interval censored data to those obtained using the non-parametric pseudo-observations for right censored data. Results: Our simulations show that the proposed method for calculating pseudo-observations provides unbiased estimates of the cumulative incidence function as well as associations with exposure variables with appropriate coverage probabilities. The analysis of the real dataset also suggests that our method provides estimates which are in agreement with estimates obtained from the right censored data. Conclusions: The proposed method for calculating pseudo-observations based on the flexible parametric approach provides a versatile solution to the specific challenges that arise with interval censored data. This solution allows regression modeling using a range of different association measures.

A flexible Bayesian non-parametric approach for fitting the odds to case II interval-censored data

2018 ◽  
Vol 88 (16) ◽  
pp. 3132-3150
Author(s):  
Yuh-Jenn Wu ◽  
Wei-Quan Fang ◽  
Li-Hsueh Cheng ◽  
Kai-Chi Chu ◽  
Yin-Tzer Shih ◽  
...  
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Parametric Approach ◽  
Interval Censored ◽  
Non Parametric

scholarly journals Regression models for interval censored data using parametric pseudo-observations

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Martin Nygård Johansen ◽  
Søren Lundbye-Christensen ◽  
Jacob Moesgaard Larsen ◽  
Erik Thorlund Parner
Keyword(s):  
Censored Data ◽  
Interval Censoring ◽  
Regression Modeling ◽  
Implantable Cardioverter Defibrillators ◽  
Interval Censored Data ◽  
Right Censored Data ◽  
Association Measures ◽  
Real Dataset ◽  
Interval Censored ◽  
Non Parametric

Abstract Background Time-to-event data that is subject to interval censoring is common in the practice of medical research and versatile statistical methods for estimating associations in such settings have been limited. For right censored data, non-parametric pseudo-observations have been proposed as a basis for regression modeling with the possibility to use different association measures. In this article, we propose a method for calculating pseudo-observations for interval censored data. Methods We develop an extension of a recently developed set of parametric pseudo-observations based on a spline-based flexible parametric estimator. The inherent competing risk issue with an interval censored event of interest necessitates the use of an illness-death model, and we formulate our method within this framework. To evaluate the empirical properties of the proposed method, we perform a simulation study and calculate pseudo-observations based on our method as well as alternative approaches. We also present an analysis of a real dataset on patients with implantable cardioverter-defibrillators who are monitored for the occurrence of a particular type of device failures by routine follow-up examinations. In this dataset, we have information on exact event times as well as the interval censored data, so we can compare analyses of pseudo-observations based on the interval censored data to those obtained using the non-parametric pseudo-observations for right censored data. Results Our simulations show that the proposed method for calculating pseudo-observations provides unbiased estimates of the cumulative incidence function as well as associations with exposure variables with appropriate coverage probabilities. The analysis of the real dataset also suggests that our method provides estimates which are in agreement with estimates obtained from the right censored data. Conclusions The proposed method for calculating pseudo-observations based on the flexible parametric approach provides a versatile solution to the specific challenges that arise with interval censored data. This solution allows regression modeling using a range of different association measures.

scholarly journals Estimation of Conditional Survival Function under Dependent Right Random Censored Data

2020 ◽  
Vol 49 (4) ◽  
pp. 1-8
Author(s):  
Abdushukurov Abdurahim Ahmedovich
Keyword(s):  
Asymptotic Normality ◽  
Gaussian Process ◽  
Censored Data ◽  
Survival Function ◽  
Conditional Survival ◽  
Random Censoring ◽  
Large Sample ◽  
Conditional Survival Function

In this paper considered problem consist in estimation of conditional survival function by right random censoring model in the presence of covariate. We propose a new estimator of conditional survival function and study its large sample properties. We present result of asymptotic normality with the same limiting Gaussian process as for copula-graphic estimator.

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